
import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


UpperCAmelCase       =					logging.get_logger(__name__)
logging.set_verbosity_info()






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
	if "xprophetnet" in prophetnet_checkpoint_path:
		lowercase	    =       XLMProphetNetForConditionalGenerationOld.from_pretrained(__SCREAMING_SNAKE_CASE				)
		lowercase    ,   lowercase	    =       XLMProphetNetForConditionalGeneration.from_pretrained(
		    __SCREAMING_SNAKE_CASE					,				output_loading_info=__SCREAMING_SNAKE_CASE				)
	else:
		lowercase	    =       ProphetNetForConditionalGenerationOld.from_pretrained(__SCREAMING_SNAKE_CASE				)
		lowercase    ,   lowercase	    =       ProphetNetForConditionalGeneration.from_pretrained(
		    __SCREAMING_SNAKE_CASE					,				output_loading_info=__SCREAMING_SNAKE_CASE				)

	lowercase	    =       ['key_proj', 'value_proj', 'query_proj']

	lowercase	    =       {
	    'self_attn': 'ngram_self_attn',
	    'cross_attn': 'encoder_attn',
	    'cross_attn_layer_norm': 'encoder_attn_layer_norm',
	    'feed_forward_layer_norm': 'final_layer_norm',
	    'feed_forward': '',
	    'intermediate': 'fc1',
	    'output': 'fc2',
	    'key_proj': 'k_proj',
	    'query_proj': 'q_proj',
	    'value_proj': 'v_proj',
	    'word_embeddings': 'embed_tokens',
	    'embeddings_layer_norm': 'emb_layer_norm',
	    'relative_pos_embeddings': 'relative_linear',
	    'ngram_embeddings': 'ngram_input_embed',
	    'position_embeddings': 'embed_positions',
	}

	for key in loading_info["missing_keys"]:
		lowercase	    =       key.split('.'				)

		if attributes[0] == "lm_head":
			lowercase	    =       prophet
			lowercase	    =       prophet_old
		else:
			lowercase	    =       prophet.prophetnet
			lowercase	    =       prophet_old.model

		lowercase	    =       False
		for attribute in attributes:
			if attribute in mapping:
				lowercase	    =       mapping[attribute]
				if not hasattr(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				) and len(__SCREAMING_SNAKE_CASE				) > 0:
					lowercase	    =       attribute
			elif hasattr(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
				lowercase	    =       attribute

			if attribute == "weight":
				assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
				lowercase	    =       old_model.weight
				logger.info(F'''{attribute} is initialized.'''				)
				lowercase	    =       True
				break
			elif attribute == "bias":
				assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
				lowercase	    =       old_model.bias
				logger.info(F'''{attribute} is initialized'''				)
				lowercase	    =       True
				break
			elif attribute in special_keys and hasattr(__SCREAMING_SNAKE_CASE					,				'in_proj_weight'				):
				lowercase	    =       old_model.in_proj_weight.shape[0] // 3
				lowercase	    =       getattr(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)
				param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
				param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
				if attribute == "query_proj":
					lowercase	    =       nn.Parameter(old_model.in_proj_weight[:embed_dim, :]				)
					lowercase	    =       nn.Parameter(old_model.in_proj_bias[:embed_dim]				)

				elif attribute == "key_proj":
					lowercase	    =       nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]				)
					lowercase	    =       nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]				)
				elif attribute == "value_proj":
					lowercase	    =       nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]				)
					lowercase	    =       nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]				)
				lowercase	    =       True
				break
			elif attribute == "position_embeddings":
				assert (
				    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
				), "Hidden size has to match"
				assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
				lowercase	    =       nn.Parameter(old_model.embed_positions.weight[:512, :]				)
				lowercase	    =       True
				break

			if attribute.isdigit():
				lowercase	    =       model[int(__SCREAMING_SNAKE_CASE				)]
				lowercase	    =       old_model[int(__SCREAMING_SNAKE_CASE				)]
			else:
				lowercase	    =       getattr(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)

				if old_attribute == "":
					lowercase	    =       old_model
				else:
					if not hasattr(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				):
						raise ValueError(F'''{old_model} does not have {old_attribute}'''				)
					lowercase	    =       getattr(__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE				)

		if not is_key_init:
			raise ValueError(F'''{key} was not correctly initialized!'''				)

	print(F'''Saving model to {pytorch_dump_folder_path}'''				)
	prophet.save_pretrained(__SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
	UpperCAmelCase       =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--prophetnet_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	UpperCAmelCase       =					parser.parse_args()
	convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)





import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

_SCREAMING_SNAKE_CASE     :					int      =       logging.getLogger()
def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							argparse.ArgumentParser()
     parser.add_argument('''-f'''							)
     snake_case											=							parser.parse_args()
     return args.f

class     A__   (       snake_case__      ):







    """simple docstring"""

    def 						a_     (			self       ):
         snake_case											=							logging.StreamHandler(sys.stdout       )
         logger.addHandler(__snake_case       )
    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							get_gpu_count()

         if n_gpu > 1:
              pass
              # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
              # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
              # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
              # cmd = [sys.executable] + distributed_args + args
              # execute_subprocess_async(cmd, env=self.get_env())
              # XXX: test the results - need to save them first into .json file
         else:
              args.insert(0     ,   '''run_glue_deebert.py'''       )
              with patch.object(__snake_case     ,   '''argv'''     ,   __snake_case       ):
                   snake_case											=							run_glue_deebert.main()
                   for value in result.values():
                        self.assertGreaterEqual(__snake_case     ,   0.666       )




    @slow
    @require_torch_non_multi_gpu
    def 						a_     (			self       ):
         snake_case											=							'''
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            '''.split()
         self.run_and_check(__snake_case       )

         snake_case											=							'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(__snake_case       )

         snake_case											=							'''
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            '''.split()
         self.run_and_check(__snake_case       )




